Automatic siting for air quality monitoring stations

ABSTRACT

A system, a computer readable storage medium, and a method for automatically siting for air quality monitoring stations includes collecting from air quality monitoring stations air pollution concentration data, collecting from meteorological stations meteorological data, and interpolating, by the at least one or more processors, the air pollution concentration data to provide interpolated air pollution concentration data. The method and system can further cluster the interpolated air pollution concentration data and the meteorological data to provide clustered data and select a candidate site for an air monitoring station using the clustered data. The method and system can further evaluate a relationship of the air pollution concentration data with the meteorological data by weighting the air pollution data with the meteorological data.

BACKGROUND

The present disclosure generally relates to a computer system andmethod, and more particularly relates to a system and method forautomatic siting for air quality monitoring stations.

Existing techniques including existing computer systems are generallynot ideally suited for accurately detecting and placing air qualitymonitoring stations. Air pollution is a serious problem for manydeveloping countries including China that will negatively impactnational health and economic conditions. Current techniques forplacement of monitoring stations typically involve the manualconsultation with experts, which is a time consuming and inaccurateprocess particularly for a system with a vast number of monitoringstations.

Air quality stations are used for air pollution monitoring andprevention and can assist in determining the space distribution asrelating to the source of the pollution, in guiding human activitiesthat impact pollution, and for forecasting the extent of the pollution.

SUMMARY

According to one embodiment of the present invention, a method forautomatically siting for air quality monitoring stations includescollecting from air quality monitoring stations air pollutionconcentration data, collecting from meteorological stationsmeteorological data, and interpolating, by the at least one or moreprocessors, the air pollution concentration data to provide interpolatedair pollution concentration data. The method and system can furthercluster the interpolated air pollution concentration data and themeteorological data to provide clustered data and select a candidatesite for an air monitoring station using the clustered data. The methodand system can further evaluate a relationship of the air pollutionconcentration data with the meteorological data by weighting the airpollution data with the meteorological data.

According to another embodiment of the present invention, a system forautomatically siting for air quality monitoring stations can include atleast one memory and at least one processor of a computer systemcommunicatively coupled to the at least one memory, the at least oneprocessor, responsive to instructions stored in memory, and beingconfigured to perform a method. The method can include collecting froman air quality monitoring stations, air pollution concentration data,collecting from meteorological stations, meteorological data,interpolating, by the at least one processor, at least the air pollutionconcentration data to provide interpolated air pollution concentrationdata, clustering, by the at least one processor, the interpolated airpollution concentration data and the meteorological data to provideclustered data, and selecting, by the at least one or more processors, acandidate site for an air monitoring station using the clustered data.

According to yet another embodiment of the present invention, anon-transitory computer readable storage medium can include computerinstructions which, responsive to being executed by one or moreprocessors, cause the processor or processors to perform operations asdescribed in the methods or systems above or elsewhere herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures, in which like reference numerals refer toidentical or functionally similar elements throughout the separateviews, and which together with the detailed description below areincorporated in and form part of the specification, serve to furtherillustrate various embodiments and to explain various principles andadvantages all in accordance with the present invention, in which:

FIG. 1 is a depiction of flow diagram of a system or method forautomatically siting for air quality monitoring stations according tovarious embodiments of the present invention;

FIG. 2 is another depiction of flow diagram of a system or method forautomatically siting for air quality monitoring stations according tovarious embodiments of the present invention;

FIG. 3 is a depiction of flow diagram of a system or method forgenerating candidates of stations that monitor pollution concentrationdata according to various embodiments of the present invention;

FIG. 4 is a depiction of flow diagram of a system or method forgenerating candidates of transform stations that monitor meteorologicaldata according to various embodiments of the present invention;

FIG. 5A is a flow diagram illustrating a method for using meteorologicaldata to find a center of meteorological fields of a single time sliceaccording to various embodiments of the present invention;

FIG. 5B is a flow diagram illustrating a method for combining differenttime slices to find a trajectory in a certain region according tovarious embodiments of the present invention;

FIG. 6 is a flow diagram illustrating a method of automatic site findingaccording to various embodiments of the present invention;

FIG. 7 is a block diagram illustrating a system automatically siting forair quality monitoring stations according to various embodiments of thepresent invention; and

FIG. 8 is another depiction of flow diagram of a method forautomatically siting for air quality monitoring stations according tovarious embodiments of the present invention.

DETAILED DESCRIPTION

As required, detailed embodiments of the present invention are disclosedherein; however, it is to be understood that the disclosed embodimentsare merely examples of the invention, which can be embodied in variousforms. Therefore, specific structural and functional details disclosedherein are not to be interpreted as limiting, but merely as a basis forthe claims and as a representative basis for teaching one skilled in theart to variously employ the present invention in virtually anyappropriately detailed structure and function. Further, the terms andphrases used herein are not intended to be limiting; but rather, toprovide an understandable description of the invention.

According to various embodiments of the present invention, disclosed isa system and method for automatic siting for air quality monitoringstations. Air pollution is a severe problem for humanity causingpotentially disastrous results from a health and economic standpoint forany number of countries. Measuring or monitoring the air quality is oneof the primary steps in reducing and preventing air pollution. In thevast regions of a number of landmasses, the number of air qualitymonitoring stations and their placement is insufficient for monitoringair pollution metrics effectively. Furthermore, appropriately siting (orplacing) the air quality monitoring stations efficiently for the bestand most accurate results is challenging. In China and other developingcountries, the situation is more urgent for national health and economiclosses and the current methods of monitoring by consulting with expertsis slow, inaccurate and inadequate. Air quality monitoring is the basisfor air pollution prevention to assist in guiding future human activity,determining adequate space distribution which serves in detectingpollution sources in industry, and for air quality forecasting.

Embodiments of the invention generally leverage meteorological and airpollution concentration data as the basis of the siting as an objectiveinput and evaluation for a different kind of air quality monitoringstation selection or siting method. The siting methods can useclustering to find the potential candidate or backup station sites andcan further use mathematical optimization methods to further refine theselection process. The embodiments can further combine meteorologicaldata with the air pollution concentration data to help find the sourceand transmission patterns of the air pollutant. The embodiments alsoconsider the change of the time into consideration for the evaluation ofdifferent station. The change of time can be seasonal (e.g., summer,autumn, winter, spring), or time of day, or day of the week, or day ofthe month, or any other time increment. The methods herein can also useclustering to find the typical sites for air quality monitoringevaluation and air pollutant transmission. In some embodiments,mathematical optimization techniques can optimize the selection ofstation sites and also use expert experience and common sense intoconstraints for use with the system. Other factors such as dataregarding roads and terrain can also be used as inputs for constraintsor for optimization for reasonable and practical outputs and solutions.The embodiments can be implemented so that siting is done rapidly andapplied for a large number of monitoring stations. In other words, themethods and systems are scalable and can be extended from one area toanother or from one city to another in a manner that is objective andreasonable.

The system is implemented as follows: 1. We Collect air pollution datafrom monitor stations and interpolate it in the dimensions of time andspace. 2. We use clustering method to get the backup site for ourselection. 3. The backup site will be much more than the stations wewant to choose. And each of the backup site will have an identificationby expert experience and other reference. 4. The mathematicaloptimization method is used to get the solution of the stations. As theproblem may be a NP-hard question, we may have an optimization functionand some of constraints for iteration.

A system or method 10 represented in FIG. 1 can include generatingcandidate air pollution monitoring stations at block 11 by collectingair pollution concentration data at 11A, interpolating the air pollutiondata in terms of time and space at block 11B, and clustering theinterpolated data to generate the candidate air pollution monitoringstations at block 11C. Concurrently or non-concurrently, the system ormethod can generate candidates of meteorological monitoring stations(also referred to as “transform stations”) at block 12 (that canmonitor, for example, air pressure cells and locations, temperature,humidity, heat, heat indexes, UV indexes, dew points, precipitation, andother weather related data parameters). The meteorological data can becollected at block 12A, and sliced or divided by time at block 12Bbefore clustering the potential meteorological monitoring stations atblock 12C. The method can further evaluate the air pollutionconcentration data in view of the meteorological data for specific timesand locations during the clustering steps in order to select a candidatesite for an air quality monitoring station at block 13. Optionally, themethod can use inputs from block 14 from expert experientialinformation, other historical data, or reference data to help in theselection of the candidate sites at block 13. All the candidate sitesare gathered and collected at block 16 where an automatic siting findermakes appropriate selections among the candidate sites using basicparameters and constraints from block 15. The basic parameters andconstraints can be user programmed or selected to suit a particularconfiguration and/or budget. The parameters or constraints can includeone or more of a number of stations, a range of evaluation stations bypercentage, range of reference stations by percentage, a range of highlypolluting stations by percentage, a range of transform stations bypercentage, or a minimum distance between every two stations. Otherparameters and constraints can be included and selected as desired for aparticular system. With the limitations set by the parameters andconstraints from block 15, the automatic siting finder from block 16 canprovide the selected air quality monitoring station (sites) at block 17.

A system or method 20 represented in FIG. 2 is similar to the system ormethod 10 of FIG. 1. The system or method 20 can include generatingcandidate air pollution monitoring stations by collecting air pollutionconcentration data at 21A, interpolating the air pollution data in termsof time and space at block 21B, and clustering the interpolated data atblock 21C. Concurrently or non-concurrently, the system or method cangenerate candidates of meteorological monitoring stations or “transformstations” at block 22A and clustering the potential meteorologicalmonitoring stations at block 22B. Optionally, the method 20 can useinputs from expert experiential information, other historical data, orreference data to help in the generation of the candidate or “backup”sites at block 23.

All the candidate sites are gathered and collected at block 26 where amathematical optimization can assist in selecting the appropriate airquality station (sites) at block 27. The mathematical optimization block26 can be similar to the automatic siting finder 16 of FIG. 1. Themathematical optimization block 26 can optionally use expertexperiential information and other constraints from block 24, basicparameters and constraints 25, and one or more optimization functions 29to assist in selecting the air quality station sites at block 27. Thebasic parameters or constraints can include similar parameters andconstraints as described with respect to block 15 of FIG. 1. The expertexperiential information and other constraints from block 24 can includethe number or range of stations, the number or range of transformstations, the number or range of transform traffic stations, the numberor range of highly polluting stations, the number or range of referencestations, the number or range of evaluation stations, or the minimumdistance between every 2 stations. Other parameters and constraints canbe included and selected as desired for a particular system. With thelimitations set by the parameters and constraints from blocks 24, 25,and/or 29, the mathematical optimization from block 26 can provide theselected air quality monitoring station (sites) at block 27.

Referring to FIGS. 1 and 3, a portion or section “A” of the overallmethod 10 in some embodiments involves generating the candidates of airpollution monitoring stations using the method 30 of FIG. 3. Method 30collects the air pollution concentration data at 31A and further dividesthe air pollution concentration data into different time slices at 31B.The time sliced data of 31B can be applied or expanded to other areas ofa particular overall area in a geographical interpolation at 31C. Aspreviously described with respect to FIG. 1, meteorological data isgathered at 32A and evaluated with respect to the air pollutionconcentration data at block 32B. The air pollution concentration datacan be weighted with the meteorological data and some stratification canbe done in terms of time and in terms of types of pollutants forexample. In this instance, the data can be stratified by seasons such asspring 33A, summer 33B, autumn, 33C, and winter 33D and by pollutantssuch as particles (PM25) 34A, particles (PM10) 34B, ozone (O3) 34C,sulfur dioxide (SO2) 34D, nitrogen dioxide (NO2) 34E, and carbonmonoxide (CO) 34F.

The method can continue by clustering using, for example k-meansclustering at block 35A. K-means clustering is a method of vectorquantization, originally from signal processing that is popular forcluster analysis in data mining. K-means clustering aims to partition nobservations into k clusters in which each observation belongs to thecluster with the nearest mean, serving as a prototype of the cluster.This results in a partitioning of the data space into cells known asVoronoi cells. Through an iterative process using decision block 35Bthat determines if the cluster meets the basic parameters andconstraints set and through further parameter adjustments at block 35C,cluster centers are gathered at block 36A. Through a further decisionblock 36B comparing the concentration of one candidate station to theaverage of all the concentration of the candidate stations and a furtherstratification by weighting, the candidate sites can be selected bytypes. For example, if the weighting is greater than a first thresholdw1 at 37A and the pollutant is determined to be nitrogen dioxide near aroad, then the candidate site can be selected as a traffic candidatemonitoring site at 37B and otherwise the candidate site can be selectedas a highly polluting candidate site at 37C. If the weighting is lessthan the first threshold w1 and greater than a second threshold w2, thenthe candidate site can be selected as an evaluation site candidate at38. If the weighing is less than the second threshold w2, then thecandidate site can be selected as reference site candidate. Theevaluation site candidate is generally considered a candidate site thatcan have some form of pollution, but generally not exceedingunacceptable limits but also not known for a generally pollutant freeenvironment. The reference site candidate is generally considered agenerally pollutant free environment or at least an environment withacceptable levels of pollutants to use as a reference in comparison toother sites.

The problem for selecting these sites is computationally difficult(NP-hard); however, there are efficient heuristic algorithms that can beemployed to converge quickly to a local optimum. These are usuallysimilar to the expectation-maximization algorithm for mixtures ofGaussian distributions via an iterative refinement approach employed byboth algorithms Additionally, they both use cluster centers to model thedata; however, k-means clustering tends to find clusters of comparablespatial extent, while the expectation-maximization mechanism allowsclusters to have different shapes. The embodiments are not limited tousing either clustering techniques and are only described herein asexamples.

Referring to FIGS. 1 and 4, a portion or section “B” of the overallmethod 10 in some embodiments involves generating the candidates ofmeteorological monitoring stations using the method 40 of FIG. 4. Method40 uses the meteorological data at block 42 to find a center ofmeteorological fields of at least one time slice. In some embodiments,the meteorological fields can be pressure cells or measurements. Themethod 40 can further combine different time slices to find thetrajectory in certain regions at block 44. At 46, the method 40 can thenmatch the trajectory of the meteorological data with the air pollutionconcentration data. At 48, the method can then perform trajectoryclustering and further generate the candidate air quality monitoringstations.

Referring to FIGS. 4 and 5A, a portion “B1” of the meteorologicalmonitoring station candidate siting method 40 that finds the center ofmeteorological field of at least one time slice at block 42 is furtherdetailed in method 50 of FIG. 5. At 51, a variable of the pressure isreduced to a Mean Sea Level (MSL) in a Final or FNL value. At 52A, theisobars of the pressure are reduced to a MSL value and at 52B, theextreme points of the pressure is reduced to a MSL value. Then, theisobars and extreme points are matched at block 53. At 54A, the Hausdoffdistance of the isobars and the extreme points are determined and at54B, the Sobel gradient of the extreme points and the nearest isobar aredetermined. Combining the metrics from 54A and 54B, a center of thefield of pressure and the scope of the field of pressure is determinedat 55A and can be represented in a map or presentation at 55B as shown.

Referring to FIGS. 4 and 5B, a portion “B2” of the meteorologicalmonitoring station candidate siting method 40 that combines thedifferent time slices to determine the trajectory of the meteorologicaldata at block 44 is further illustrated by the representation 57 of FIG.5B. FIG. 5B illustrates various time slices of maps including the timeslice of 6:00:00 on Oct. 3, 2015 at 58A, the time slice of 12:00:00 onOct. 3, 2015 at 58B and the time slice of 6:00:00 on Oct. 4, 2015 at58C. These time slice representations are combined to determine andrepresent the trajectory of the meteorological data as illustrated by59.

Referring to FIGS. 1 and 6, a portion “C” of the overall method 10 insome embodiments involves a particular method of automatic site finding60 similar in operation to the automatic siting finder 16 of FIG. 1. Themethod 60 begins by collecting all the candidate sites at 61 andapplying basic parameters and constraints 62 to additional steps 63A-Das shown. More particularly, at 63A, a weight can be calculated for agiven candidate site based on a reshaping and clustering result.Reshaping can be done in addition to clustering to further assist inselecting a candidate site based on the meteorological and pollutionconcentration data. At 63B, with the use of the basic parameters andconstraints as well as the weighting information, some of the candidatesites can be removed and a first set of candidates can be provided at63C. An optimization function can be further applied at 63D such as thefunction for calculating a object function, for example,Obj=(Max(Distance d between every 2 candidates)−sum (weight))/(The wholearea of all candidates). The optimization function can be repeated atdecision block 64 until a maximum iteration is reached. Once the maximumiteration is reached at block 64, the final output of the site locationand corresponding information is provided at 65.

In some embodiments, a system includes at least one memory and at leastone processor of a computer system communicatively coupled to the atleast one memory. The at least one processor can be configured toperform a method including methods described above.

According yet to another embodiment of the present invention, a computerreadable storage medium comprises computer instructions which,responsive to being executed by one or more processors, cause the one ormore processors to perform operations as described in the methods orsystems above or elsewhere herein.

As shown in FIG. 37, an information processing system 100 of a system700 can be communicatively coupled with the air quality monitoring siteselection module 702 and a group of client or other devices, or coupledto a presentation device for display at any location at a terminal orserver location. According to this example, at least one processor 102,responsive to executing instructions 107, performs operations tocommunicate with the site selection module 702 via a bus architecture208, as shown. The at least one processor 102 is communicatively coupledwith main memory 104, persistent memory 106, and a computer readablemedium 120. The processor 102 is communicatively coupled with anAnalysis & Data Storage 115 that, according to various implementations,can maintain stored information used by, for example, the site selectionmodule 702 and more generally used by the information processing system100. Optionally, for example, this stored information can includeinformation received from the client or other devices. For example, thisstored information can be received periodically from the client devicesand updated or processed over time in the Analysis & Data Storage 115.Additionally, according to another example, a history log can bemaintained or stored in the Analysis & Data Storage 115 of theinformation processed over time. The site selection module 702, and theinformation processing system 100, can use the information from thehistory log such as in the analysis process and in making decisionsrelated determining similarity and to retrieving similar images.

The computer readable medium 120, according to the present example, canbe communicatively coupled with a reader/writer device (not shown) thatis communicatively coupled via the bus architecture 208 with the atleast one processor 102. The instructions 107, which can includeinstructions, configuration parameters, and data, may be stored in thecomputer readable medium 120, the main memory 104, the persistent memory106, and in the processor's internal memory such as cache memory andregisters, as shown.

The information processing system 100 includes a user interface 110 thatcomprises a user output interface 112 and user input interface 114.Examples of elements of the user output interface 112 can include adisplay, a speaker, one or more indicator lights, one or moretransducers that generate audible indicators, and a haptic signalgenerator. Examples of elements of the user input interface 114 caninclude a keyboard, a keypad, a mouse, a track pad, a touch pad, amicrophone that receives audio signals, a camera, a video camera, or ascanner that scans images. The received audio signals or scanned images,for example, can be converted to electronic digital representation andstored in memory, and optionally can be used with corresponding voice orimage recognition software executed by the processor 102 to receive userinput data and commands, or to receive test data for example.

A network interface device 116 is communicatively coupled with the atleast one processor 102 and provides a communication interface for theinformation processing system 100 to communicate via one or morenetworks 108. The networks 108 can include wired and wireless networks,and can be any of local area networks, wide area networks, or acombination of such networks. For example, wide area networks includingthe internet and the web can inter-communicate the informationprocessing system 100 with other one or more information processingsystems that may be locally, or remotely, located relative to theinformation processing system 100. It should be noted that mobilecommunications devices, such as mobile phones, Smart phones, tabletcomputers, lap top computers, and the like, which are capable of atleast one of wired and/or wireless communication, are also examples ofinformation processing systems within the scope of the presentinvention. The network interface device 116 can provide a communicationinterface for the information processing system 100 to access the atleast one database 117 according to various embodiments of theinvention.

The instructions 107, according to the present example, can includeinstructions for monitoring, instructions for analyzing, instructionsfor retrieving and sending information and related configurationparameters and data. It should be noted that any portion of theinstructions 107 can be stored in a centralized information processingsystem or can be stored in a distributed information processing system,i.e., with portions of the system distributed and communicativelycoupled together over one or more communication links or networks.

FIG. 8 illustrates an example of a method, according to variousembodiments of the present invention, which can operate in conjunctionwith the information processing system of FIG. 7. Specifically,according to the example shown in FIG. 8, a method 800 for siting airquality monitoring stations can include collecting air pollutionconcentration data from air quality monitoring stations at block 802 andcollecting meteorological data from meteorological stations at 804. Themethod 800 can further include at block 806, interpolating the airpollution concentration data to provide interpolated air pollutionconcentration data. At block 808, the method can cluster theinterpolated air pollution concentration data and the meteorologicaldata to provide clustered data. At block 810, the method can iterativelyselect a candidate site for an air monitoring station using theclustered data. As part of the process, the candidate site can bequalified as one of a regular air quality monitoring station site as anevaluation site candidate (see 38 of FIG. 3), a reference site candidate(see 39 of FIG. 3), a traffic site candidate (see 37B of FIG. 3), or ahighly polluting site candidate (see 37C of FIG. 3). Of course, thecandidate site can be qualified as any other type of candidate sitebased on the parameters and stratification that a system designer canimplement within contemplation of the various embodiments.

Non-Limiting Examples

The examples provide herein may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Although the present specification may describe components and functionsimplemented in the embodiments with reference to particular standardsand protocols, the invention is not limited to such standards andprotocols. Each of the standards represents examples of the state of theart. Such standards are from time-to-time superseded by faster or moreefficient equivalents having essentially the same functions.

The illustrations of examples described herein are intended to provide ageneral understanding of the structure of various embodiments, and theyare not intended to serve as a complete description of all the elementsand features of apparatus and systems that might make use of thestructures described herein. Many other embodiments will be apparent tothose of skill in the art upon reviewing the above description. Otherembodiments may be utilized and derived therefrom, such that structuraland logical substitutions and changes may be made without departing fromthe scope of this invention. Figures are also merely representationaland may not be drawn to scale. Certain proportions thereof may beexaggerated, while others may be minimized. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement calculated toachieve the same purpose may be substituted for the specific embodimentsshown. The examples herein are intended to cover any and all adaptationsor variations of various embodiments. Combinations of the aboveembodiments, and other embodiments not specifically described herein,are contemplated herein.

The Abstract is provided with the understanding that it is not intendedbe used to interpret or limit the scope or meaning of the claims. Inaddition, in the foregoing Detailed Description, various features aregrouped together in a single example embodiment for the purpose ofstreamlining the invention. This method of invention is not to beinterpreted as reflecting an intention that the claimed embodimentsrequire more features than are expressly recited in each claim. Rather,as the following claims reflect, inventive subject matter lies in lessthan all features of a single disclosed embodiment. Thus the followingclaims are hereby incorporated into the Detailed Description, with eachclaim standing on its own as a separately claimed subject matter.

Although only one processor is illustrated for an information processingsystem, information processing systems with multiple CPUs or processorscan be used equally effectively. Various embodiments of the presentinvention can further incorporate interfaces that each includesseparate, fully programmed microprocessors that are used to off-loadprocessing from the processor. An operating system (not shown) includedin main memory for the information processing system may be a suitablemultitasking and/or multiprocessing operating system, such as, but notlimited to, any of the Linux, UNIX, Windows, and Windows Server basedoperating systems. Various embodiments of the present invention are ableto use any other suitable operating system. Various embodiments of thepresent invention utilize architectures, such as an object orientedframework mechanism, that allows instructions of the components ofoperating system (not shown) to be executed on any processor locatedwithin the information processing system. Various embodiments of thepresent invention are able to be adapted to work with any datacommunications connections including present day analog and/or digitaltechniques or via a future networking mechanism.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. The term “another”, as used herein,is defined as at least a second or more. The terms “including” and“having,” as used herein, are defined as comprising (i.e., openlanguage). The term “coupled,” as used herein, is defined as“connected,” although not necessarily directly, and not necessarilymechanically. “Communicatively coupled” refers to coupling of componentssuch that these components are able to communicate with one anotherthrough, for example, wired, wireless or other communications media. Theterms “communicatively coupled” or “communicatively coupling” include,but are not limited to, communicating electronic control signals bywhich one element may direct or control another. The term “configuredto” describes hardware, software or a combination of hardware andsoftware that is adapted to, set up, arranged, built, composed,constructed, designed or that has any combination of thesecharacteristics to carry out a given function. The term “adapted to”describes hardware, software or a combination of hardware and softwarethat is capable of, able to accommodate, to make, or that is suitable tocarry out a given function.

The terms “controller”, “computer”, “processor”, “server”, “client”,“computer system”, “computing system”, “personal computing system”,“processing system”, or “information processing system”, describeexamples of a suitably configured processing system adapted to implementone or more embodiments herein. Any suitably configured processingsystem is similarly able to be used by embodiments herein, for exampleand not for limitation, a personal computer, a laptop personal computer(laptop PC), a tablet computer, a smart phone, a mobile phone, awireless communication device, a personal digital assistant, aworkstation, and the like. A processing system may include one or moreprocessing systems or processors. A processing system can be realized ina centralized fashion in one processing system or in a distributedfashion where different elements are spread across severalinterconnected processing systems.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription herein has been presented for purposes of illustration anddescription, but is not intended to be exhaustive or limited to theexamples in the form disclosed. Many modifications and variations willbe apparent to those of ordinary skill in the art without departing fromthe scope of the examples presented or claimed. The disclosedembodiments were chosen and described in order to explain the principlesof the embodiments and the practical application, and to enable othersof ordinary skill in the art to understand the various embodiments withvarious modifications as are suited to the particular use contemplated.It is intended that the appended claims below cover any and all suchapplications, modifications, and variations within the scope of theembodiments.

What is claimed is:
 1. A computer implemented method, comprising:collecting from air quality monitoring stations, by at least one or moreprocessors of at least one computing device, air pollution concentrationdata; collecting from meteorological stations, by the at least one ormore processors, meteorological data; interpolating, by the at least oneor more processors, at least the air pollution concentration data toprovide interpolated air pollution concentration data; clustering, bythe at least one or more processors, the interpolated air pollutionconcentration data and the meteorological data to provide clustereddata; and selecting, by the at least one or more processors, a candidatesite for an air monitoring station using the clustered data.
 2. Thecomputer implemented method of claim 1, wherein the interpolatingcomprises dividing the air pollution concentration data by time andapplying time sliced data to different geographic areas.
 3. The computerimplemented method of claim 1, wherein the method further comprisesevaluating a relationship of the air pollution concentration data withthe meteorological data by weighting the air pollution concentrationdata with the meteorological data.
 4. The computer implemented method ofclaim 1, wherein the method further comprises weighting themeteorological data by season of the year and the air pollution data bytype of pollutant.
 5. The computer implemented method of claim 1,wherein the method further comprises using meteorological data to find acenter of meteorological fields of one time slice, combining a differenttime slice to find a trajectory of a meteorological condition in acertain region, matching the trajectory with the air pollutionconcentration data and trajectory clustering, and generating at leastone of the candidate sites.
 6. The computer implemented method of claim1, wherein the method further comprises automatically generating acenter of a field of pressure and a scope of the field of pressure. 7.The computer implemented method of claim 1, wherein the method furthercomprises automatically generating a center of a field of pressure and ascope of the field of pressure by matching isobars of pressure andextreme points of pressure, determining the Hausdoff distance of isobarsand extreme points and determining the Sobel gradient of the extremepoints and a nearest isobar.
 8. The computer implemented method of claim1, further comprising applying parameters and constraints to anautomatic site finder after the automatic site finder receives all thecandidate sites.
 9. The computer implemented method of claim 8, whereinthe parameters and constraints comprises calculating a weight of acandidate site based on a reshaping and clustering result, anditeratively calculating an object function to select a final output ofthe candidate site or candidate sites.
 10. The computer implementedmethod of claim 8, wherein the parameters comprises one or more of anumber of stations, a range of evaluation stations by percentage, rangeof reference stations by percentage, a range of highly pollutingstations by percentage, a range of transform stations by percentage, ora minimum distance between every two stations.
 11. A system comprising:at least one memory; and at least one processor of a computer systemcommunicatively coupled to the at least one memory, the at least oneprocessor, responsive to instructions stored in memory, being configuredto perform a method comprising: collecting from an air qualitymonitoring stations, air pollution concentration data; collecting frommeteorological stations, meteorological data; interpolating, by the atleast one processor, at least the air pollution concentration data toprovide interpolated air pollution concentration data; clustering, bythe at least one processor, the interpolated air pollution concentrationdata and the meteorological data to provide clustered data; andselecting, by the at least one or more processors, a candidate site foran air monitoring station using the clustered data.
 12. The system ofclaim 11, further comprising instructions stored in memory which whenexecuted by the at least one processor causes the at least one processorto perform the operation of evaluating a relationship of the airpollution concentration data with the meteorological data by weightingthe air pollution concentration data with the meteorological data. 13.The system of claim 11, wherein the instructions stored in memory whichwhen executed by the at least one processor causes the at least oneprocessor to perform the operation of weighting the meteorological databy time of the year and the air pollution data by type of pollutant. 14.The system of claim 11, wherein the instructions stored in memory whichwhen executed by the at least one processor causes the at least oneprocessor to perform the operation of using meteorological data to finda center of meteorological fields of one time slice, combining adifferent time slice to find a trajectory of a meteorological conditionin a certain region, matching the trajectory with the air pollutionconcentration data and trajectory clustering, and generating at leastone of the candidate sites.
 15. The system of claim 11, wherein theinstructions stored in memory which when executed by the at least oneprocessor causes the at least one processor to perform the operation ofautomatically generating a center of a field of pressure and a scope ofthe field of pressure by matching isobars of pressure and extreme pointsof pressure, determining the Hausdoff distance of isobars and extremepoints and determining the Sobel gradient of the extreme points and anearest isobar.
 16. The system of claim 15, wherein the instructionsstored in memory which when executed by the at least one processorcauses the at least one processor to further perform applying parametersand constraints to an automatic site finder after the automatic sitefinder receives all the candidate sites.
 17. A non-transitorycomputer-readable storage medium having stored therein instructionswhich, when executed by at least one or more processors of at least onecomputing device, cause a computer system to perform a methodcomprising: collecting from air quality monitoring stations, by at leastone or more processors of at least one computing device, air pollutionconcentration data; collecting from meteorological stations, by the atleast one or more processors, meteorological data; interpolating, by theat least one or more processors, at least the air pollutionconcentration data to provide interpolated air pollution concentrationdata; clustering, by the at least one or more processors, theinterpolated air pollution concentration data and the meteorologicaldata to provide clustered data; and selecting, by the at least one ormore processors, a candidate site for an air monitoring station usingthe clustered data.
 18. The non-transitory computer-readable storagemedium of claim 17, further comprising weighting, by the at least one ormore processors, the meteorological data by time of the year and the airpollution data by type of pollutant.
 19. The non-transitorycomputer-readable storage medium of claim 17, further comprisingapplying parameters and constraints to an automatic site finder afterthe automatic site finder receives all the candidate sites.
 20. Thenon-transitory computer-readable storage medium of claim 17, furthercomprising automatically generating a center of a field of pressure anda scope of the field of pressure by matching isobars of pressure andextreme points of pressure, determining the Hausdoff distance of isobarsand extreme points and determining the Sobel gradient of the extremepoints and a nearest isobar.